← 研踪 PaperTrailarXiv:2411.19092 · v3
Neural Window Decoding of Spatially Coupled LDPC Codes: Exploiting Window Structure for Learning and Adaptation
首次提交 2024-11-28 20:13
版本更新 2026-11-07 14:49
雷达观测 2026-11-10 12:17
摘要
We propose a neural window decoder (NWD) for spatially coupled low-density parity-check (SC-LDPC) codes. The proposed methods adapt neural min-sum decoding to the structure and sequential operation of window decoding. First, target-oriented training restricts the loss to the variable nodes whose decisions are required at each window stage. Second, learned damping factors identify check-node updates that can reuse messages from the preceding iteration. Finally, adaptive weight switching selects weights trained for unreliable inherited information when a previous-window failure is detected. For a length-$20{,}000$ $(3,6)$-regular SC-LDPC code, the target-oriented NWD achieves performance comparable to sum-product window decoding while using min-sum updates. On the reduced target graph, scheduling based on regularized damping factors yields an estimated computational cost 48\% lower than that of full-graph weighted min-sum decoding, while maintaining comparable decoding performance. Adaptive weight switching improves decoding performance by 35\% at 3.0 dB relative to plain weights. These learning methods exploit the properties of window decoding to improve the reliability and computational efficiency of SC-LDPC convolutional codes.
作者备注：12 pages, 10 figures
观测到的版本
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v3 · 2026-11-07 14:49 · 最新观测版本
Neural Window Decoding of Spatially Coupled LDPC Codes: Exploiting Window Structure for Learning and Adaptation
We propose a neural window decoder (NWD) for spatially coupled low-density parity-check (SC-LDPC) codes. The proposed methods adapt neural min-sum decoding to the structure and sequential operation of window decoding. First, target-oriented training restricts the loss to the variable nodes whose decisions are required at each window stage. Second, learned damping factors identify check-node updates that can reuse messages from the preceding iteration. Finally, adaptive weight switching selects weights trained for unreliable inherited information when a previous-window failure is detected. For a length-$20{,}000$ $(3,6)$-regular SC-LDPC code, the target-oriented NWD achieves performance comparable to sum-product window decoding while using min-sum updates. On the reduced target graph, scheduling based on regularized damping factors yields an estimated computational cost 48\% lower than that of full-graph weighted min-sum decoding, while maintaining comparable decoding performance. Adaptive weight switching improves decoding performance by 35\% at 3.0 dB relative to plain weights. These learning methods exploit the properties of window decoding to improve the reliability and computational efficiency of SC-LDPC convolutional codes.
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